The Emotional Faces of the Public Opinion

The Problem

• Due to the design of attentional-based algorithms, most social media users experience echo chambers

• This minimizes the ability to understand a complete picture of public opinion(and narrows individual opinion)

• Is there a way to quickly access a different side to the story?

The Aim

• This project's aim is to input a word/trend/topic or hashtag, and a sentiment (positive, negative, or many others). The model would generate bullet points summarising the key points of the subject with the appropriate sentiment, based on public opinion. (If possible could even add a location parameter)

• Twitter is the home of contemporary public opinion, and therefore the perfect place to start

• Let's look at some data to get a better understanding

Or WordCloud ??!

Twitter Data

Authentification keys

import tweepy
from tweepy import OAuthHandler
import pandas as pd


print("You got this!")



auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth, wait_on_rate_limit=True)



# count = 1

You got this!

Search/queries

Let's use a simple example of Licorice Pizza, an award nominated but highly controversial movie recently released.

tweets = []

for tweet in tweepy.Cursor(api.search_tweets, q = "#LicoricePizza", count=10, since='2022-01-28', lang = "en").items(200):

#     print(count)
#     count += 1

    try: 
        data = [tweet.created_at, tweet.id, tweet.text, tweet.retweet_count, tweet.favorite_count, tweet.lang]
        data = tuple(data)
        tweets.append(data)

    except tweepy.TweepError as e:
        print(e.reason)
        continue

    except StopIteration:
        break

# df = pd.DataFrame(tweets, columns = ['created_at','tweet_id', 'tweet_text', "retweet_count", "favorite_count", "lang"])

# """Add the path to the folder you want to save the CSV file in as well as what you want the CSV file to be named inside the single quotations"""
# df.to_csv(path_or_buf = '/Users/caselyhayford/Desktop/Twitter Experiments/Tweets.csv/', index=False) 

Just some of the query possibilities:

DataFrame Work

df = pd.DataFrame(tweets, columns = ['created_at','tweet_id', 'tweet_text', "retweet_count", "favorite_count", "lang"])

df.shape

(200, 6)

df.head(10)

	created_at	tweet_id	tweet_text	retweet_count	favorite_count	lang
0	2022-02-19 11:20:56+00:00	1494995410510823424	A 15-year-old falling in love with a 25-year-o...	0	0	en
1	2022-02-19 11:20:47+00:00	1494995374364405760	"Don't be creepy," says the 25 year old to a 1...	0	0	en
2	2022-02-19 11:11:40+00:00	1494993078721159169	RT @universaluk: The nominations are in. #Lico...	2	0	en
3	2022-02-19 11:08:21+00:00	1494992245241442304	#LicoricePizza is too good	0	0	en
4	2022-02-19 10:59:34+00:00	1494990035552112642	RT @cineastmemes: Finally it's now available i...	1	0	en
5	2022-02-19 10:34:34+00:00	1494983743538630658	now watching #LicoricePizza FINALLY!!!! https:...	0	1	en
6	2022-02-19 10:08:17+00:00	1494977127514447874	#LicoricePizza is now on vod I repeat #Licoric...	0	0	en
7	2022-02-19 10:02:28+00:00	1494975663542915075	Finally it's now available in our channel the ...	1	2	en
8	2022-02-19 10:01:39+00:00	1494975461515923460	watching #LicoricePizza and there’s an actual ...	0	0	en
9	2022-02-19 09:57:46+00:00	1494974480262729729	Finallllllly #LicoricePizza 😌😌 https://t.co/Dn...	0	0	en

Example of Sentiment opinions

print(df["tweet_text"][0])

A 15-year-old falling in love with a 25-year-old should not be normalized or romanticized regardless of gender. #LicoricePizza

A negative sentiment highlighting the age gap issue

print(df["tweet_text"][199])

What a fantastic soundtrack (lovely cover too)! Discovered new favourites thanks to Paul Thomas Anderson. Can't wai… https://t.co/mguHg4buyL

print(df["tweet_text"][3])

#LicoricePizza is too good

Some positive tweets, talking about quality and the amazing soundtrack

Modelling, Sentimental Analysis and Text Generation

• Two Models would potentially have to be built:

  • One that would predict the sentiment of a tweet and place that tweet in the appropriate sentiment category

  • The other would take the appropriate sentiment category of tweets and generate output

    • The more advanced (very complicated) goal would be for the output to be generated summary bulletpoints (text generation).

  • Otherwise a simpler approach would be to generate key words or a wordcloud, most likely using LDA topic modelling.

Transfer Learning

• Luckily we are not the first to play around with such ideas and we will be able to use transfer learning to help us!

Thank You for listening !!!